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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1416-1421

Parallel deep convolution neural network optimization algorithm based on big data

Mao Yimin1
Zhang Ruipeng1
Cao Wenliang2
1. School of Science Faculty, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
2. Dept. of Computer Engineering, Dongguan Polytechnic, Dongguan Guangdong 523808, China
DOI: 10.19734/j.issn.1001-3695.2020.04.0112

Abstract

Aiming at the problems of too many redundant parameters, slow convergence speed and low parallel efficiency of parallel DCNN algorithm in big data environment, this paper proposed a parallel deep convolutional neural network optimization algorithm named PDCNNO. Firstly, the algorithm designed PFM strategy, pretraining network, and obtained the compressed network, which effectively reduced redundant parameters and reduced the time and space complexity of DCNN training. Secondly, it designed a CGMSE to obtain local classification results, which realized rapid convergence of conjugate gradient method and improved the convergence speed of the network. Finally, in the reduce phase, it proposed a LBRLA strategy to obtain the global classification results, which realized the fast and uniform grouping of data and improved the acceleration ratio of the parallel system. Experiments show that the algorithm not only reduces the time and space complexity of DCNN training in the big data environment, but also improves the parallelization performance of the parallel system.

Foundation Support

国家重点研发计划资助项目(2018YFC1504705)
国家自然科学基金资助项目(41562019)
广东省普通高校特色创新(自然科学)项目(2019GKTSCX142,2017GKTSCX101)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.04.0112
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Algorithm Research & Explore
Pages: 1416-1421
Serial Number: 1001-3695(2021)05-024-1416-06

Publish History

[2021-05-05] Printed Article

Cite This Article

毛伊敏, 张瑞朋, 曹文梁. 基于大数据的并行化深度卷积神经网络优化算法 [J]. 计算机应用研究, 2021, 38 (5): 1416-1421. (Mao Yimin, Zhang Ruipeng, Cao Wenliang. Parallel deep convolution neural network optimization algorithm based on big data [J]. Application Research of Computers, 2021, 38 (5): 1416-1421. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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